Using Machine Learning in Order to Improve Automatic SIMD Instruction Generation

Trouvé, Antoine; Cruz, Arnaldo J.; Fukuyama, Hiroki; Maki, Jun; Clarke, Hadrien A.; Murakami, Kazuaki; Arai, Masataka; Nakahira, Tadashi; Yamanaka, Eiji

doi:10.1016/j.procs.2013.05.296

Cited by 4 publications

(1 citation statement)

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“…An application with nested loops can be compiled depending on the available compiler, hardware and run-time system support [8][9]. The conditional statements nested loops can be vectorized depending on the loops structure and the available compiler with optimization option flags [10][11].…”

Section: Machine Learningmentioning

confidence: 99%

Improving Program Performance via Auto-Vectorization of Loops with Conditional Statements with GCC Compiler Setting

Zhu

2013

AMM

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The GCC is an auto-vectorization compiler across iterations of loops to parallelism data. Turning GCC compiler optimizations flags for auto-vectorization is a way to improve the performance ability, which is a popular approach to speed up program performance. However, there are many options in GCC compiler and selecting the best combination of these options to improve program performance through vectorization is non-trivial ( as the search space is very large ).In this work we focus on the selection of compiler transformations to auto-vectorize loops with conditional statements. The selection of compiler transformations is based on the correlation between program features, speed-up, and the analysis of the code generated and a small number of passes of iterative compilation. Our preliminary experimental results show that proposed technique attains performance improvements the best ~ 6x using loops in the TSVC benchmark suite on the state-of-the-art Intel Core i3 processor.

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Section: Machine Learningmentioning

confidence: 99%